Conventional methods of hybrid video coding either encode a video frame by itself, without reference to other frames in the sequence, or encode a video frame by predicting it from other, already coded and reconstructed, frames in the sequence. This process routinely operates at block-level in the images. In either case, the frames that are reconstructed following the coding process contain distortions introduced by this process, which can be modeled as noise. This noise may manifest itself visibly as coding artifacts, such as blocking and ringing artifacts. Some of the reconstructed frames stored in a frame store typically used by a video coder and decoder are further used as reference for predicting other frames in the sequence while others are not. In the previous case, the quality of the reconstructed frame stored in the frame store is not only important for its display but, through its use in the prediction process, influences the quality of subsequently coded frames which rely on it for reference purposes. In the latter case, for frames that are not further used as reference, their quality is important for display purposes. Thus, in either case, a procedure to remove the coding noise is beneficial for the quality of the reconstructed video sequence and an increase in the objective coding efficiency.
Most modern codecs include some type of filtering to reduce the level of resultant coding noise. The filtering can operate outside or inside of the coding loop in a codec. For loop filters which operate inside the coding loop, generally the same operation is performed in the encoder and in the decoder using the same available data, such that no side information specific to this filtering needs to be transmitted explicitly from the coder to the decoder. Noise filtering methods commonly used include simple low-pass filtering of the reconstructed frame data to smooth out the images and decrease the visibility of coding artifacts, and loop filters that adapt their strength based on local image characteristics (such as block edges).
Out-of-coding-loop filtering techniques, such as a lowpass filtering of a reconstructed (noisy) frame at the decoder are usually not sufficient for improving the overall image quality of the decoded video sequence. See, H. C. Reeve and J. S. Lim, “Reduction of Blocking Effects in Image Coding,” Opt. Eng., Vol. 23, no. 1, pp. 34-37. January/February 1984. In-loop filters that perform filtering inside the coding and decoding loop in an encoder and decoder, respectively, have superior performance. For more information, see Wiegand, et al., “Overview of the H.264/AVC Video Coding Standard,” IEEE Transactions on Circuits and Systems for Video Technology, on page(s): 560-576, vol. 13, Issue: 7, Jul. 2003. They usually adapt the filtering strength in a heuristic manner, based on the quantization regime used by the video codec and the characteristics of the signal being filtered. Also, since they operate in the coding loop, they cause an improvement in the quality of the reference frames used for prediction, thus improving the efficiency of the coding process.
In contrast to operating heuristically, there exist filtering techniques that are based on signal estimation from noise using a particular signal model. For more information, see A. Papoulis, “Probability, Random Variables, and Stochastic Processes”, 3rd edition, New York, McGraw-Hill, 1991.
Overcomplete denoising techniques that operate in the transform domain provide additionally the advantage of determining multiple reconstructed instances (estimates) for the same sample position in a frame, which can then be combined (e.g., averaged) in order to improve the estimation quality. See, R. R. Coifman and D. L. Donoho, “Translation Invariant Denoising,” in Wavelets and Statistics, Springer Lecture Notes in Statistics 103, pp. 125-150, New York, Springer Verlag. Existing denoising approaches, which attempt to extract the signal from coding noise, have at their core a signal estimator. Since usually the statistics of the problem are not known exactly, it is necessary to make some assumptions about the unknowns and choose a strategy for estimating the signal. One of the most popular strategies is to optimize the signal estimation for the worst-possible choice of unknowns, resulting in robust estimators. For more information, see Y. C. Eldar, A. B.-Tal, and A. Nemirovski, “Linear Minimax Regret Estimation of Deterministic Parameters with Bounded Data Uncertainties,” IEEE Transactions on Signal Processing, Vol. 52, No. 8, August 2004. It is well-known that under this design constraint, the estimators that result tend to be too conservative because the performance is optimized for the worst-case scenario.
The performance of prior methods of coding noise filtering is limited by some intrinsic characteristics of these methods and the assumptions made about the nature of the noise. Simple low-pass filtering techniques that are applied to reduce coding artifacts are not effective in handling the diversity of visual information in video frames, and tend to have widely-varying performance for these sequences (lack of performance control). Adaptive, in-loop boundary filtering in video coding uses heuristics that do not guarantee optimal solutions in some well-defined sense. Also, existing denoising techniques based on the use of a signal model, determine estimators under unrealistic assumptions about the nature of the signal and noise, in particular by assuming that the signal and noise are uncorrelated. This is not the case for the signal and coding noise encountered in image and video coding. The performance of these filtering techniques suffers, since they are poorly matched to the existing problem conditions.